Value expressions are used in a variety of contexts, such
as in the target list of the SELECT
command, as
new column values in INSERT
or
UPDATE
, or in search conditions in a number of
commands. The result of a value expression is sometimes called a
scalar, to distinguish it from the result of
a table expression (which is a table). Value expressions are
therefore also called scalar expressions (or
even simply expressions). The expression
syntax allows the calculation of values from primitive parts using
arithmetic, logical, set, and other operations.
A value expression is one of the following:
A constant or literal value
A column reference
A positional parameter reference, in the body of a function definition or prepared statement
A subscripted expression
A field selection expression
An operator invocation
A function call
An aggregate expression
A window function call
A type cast
A collation expression
A scalar subquery
An array constructor
A row constructor
Another value expression in parentheses (used to group subexpressions and override precedence)
In addition to this list, there are a number of constructs that can
be classified as an expression but do not follow any general syntax
rules. These generally have the semantics of a function or
operator and are explained in the appropriate location in 9장. An example is the IS NULL
clause.
We have already discussed constants in 4.1.2절. The following sections discuss the remaining options.
A column can be referenced in the form:
correlation
.columnname
correlation
is the name of a
table (possibly qualified with a schema name), or an alias for a table
defined by means of a FROM
clause.
The correlation name and separating dot can be omitted if the column name
is unique across all the tables being used in the current query. (See also 7장.)
A positional parameter reference is used to indicate a value that is supplied externally to an SQL statement. Parameters are used in SQL function definitions and in prepared queries. Some client libraries also support specifying data values separately from the SQL command string, in which case parameters are used to refer to the out-of-line data values. The form of a parameter reference is:
$number
For example, consider the definition of a function,
dept
, as:
CREATE FUNCTION dept(text) RETURNS dept AS $$ SELECT * FROM dept WHERE name = $1 $$ LANGUAGE SQL;
Here the $1
references the value of the first
function argument whenever the function is invoked.
If an expression yields a value of an array type, then a specific element of the array value can be extracted by writing
expression
[subscript
]
or multiple adjacent elements (an “array slice”) can be extracted by writing
expression
[lower_subscript
:upper_subscript
]
(Here, the brackets [ ]
are meant to appear literally.)
Each subscript
is itself an expression,
which will be rounded to the nearest integer value.
In general the array expression
must be
parenthesized, but the parentheses can be omitted when the expression
to be subscripted is just a column reference or positional parameter.
Also, multiple subscripts can be concatenated when the original array
is multidimensional.
For example:
mytable.arraycolumn[4] mytable.two_d_column[17][34] $1[10:42] (arrayfunction(a,b))[42]
The parentheses in the last example are required. See 8.15절 for more about arrays.
If an expression yields a value of a composite type (row type), then a specific field of the row can be extracted by writing
expression
.fieldname
In general the row expression
must be
parenthesized, but the parentheses can be omitted when the expression
to be selected from is just a table reference or positional parameter.
For example:
mytable.mycolumn $1.somecolumn (rowfunction(a,b)).col3
(Thus, a qualified column reference is actually just a special case of the field selection syntax.) An important special case is extracting a field from a table column that is of a composite type:
(compositecol).somefield (mytable.compositecol).somefield
The parentheses are required here to show that
compositecol
is a column name not a table name,
or that mytable
is a table name not a schema name
in the second case.
You can ask for all fields of a composite value by
writing .*
:
(compositecol).*
This notation behaves differently depending on context; see 8.16.5절 for details.
There are three possible syntaxes for an operator invocation:
expression operator expression (binary infix operator) |
operator expression (unary prefix operator) |
expression operator (unary postfix operator) |
where the operator
token follows the syntax
rules of 4.1.3절, or is one of the
key words AND
, OR
, and
NOT
, or is a qualified operator name in the form:
OPERATOR(
schema
.
operatorname
)
Which particular operators exist and whether they are unary or binary depends on what operators have been defined by the system or the user. 9장 describes the built-in operators.
The syntax for a function call is the name of a function (possibly qualified with a schema name), followed by its argument list enclosed in parentheses:
function_name
([expression
[,expression
... ]] )
For example, the following computes the square root of 2:
sqrt(2)
The list of built-in functions is in 9장. Other functions can be added by the user.
When issuing queries in a database where some users mistrust other users, observe security precautions from 10.3절 when writing function calls.
The arguments can optionally have names attached. See 4.3절 for details.
A function that takes a single argument of composite type can
optionally be called using field-selection syntax, and conversely
field selection can be written in functional style. That is, the
notations col(table)
and table.col
are
interchangeable. This behavior is not SQL-standard but is provided
in PostgreSQL because it allows use of functions to
emulate “computed fields”. For more information see
8.16.5절.
An aggregate expression represents the application of an aggregate function across the rows selected by a query. An aggregate function reduces multiple inputs to a single output value, such as the sum or average of the inputs. The syntax of an aggregate expression is one of the following:
aggregate_name
(expression
[ , ... ] [order_by_clause
] ) [ FILTER ( WHEREfilter_clause
) ]aggregate_name
(ALLexpression
[ , ... ] [order_by_clause
] ) [ FILTER ( WHEREfilter_clause
) ]aggregate_name
(DISTINCTexpression
[ , ... ] [order_by_clause
] ) [ FILTER ( WHEREfilter_clause
) ]aggregate_name
( * ) [ FILTER ( WHEREfilter_clause
) ]aggregate_name
( [expression
[ , ... ] ] ) WITHIN GROUP (order_by_clause
) [ FILTER ( WHEREfilter_clause
) ]
where aggregate_name
is a previously
defined aggregate (possibly qualified with a schema name) and
expression
is
any value expression that does not itself contain an aggregate
expression or a window function call. The optional
order_by_clause
and
filter_clause
are described below.
The first form of aggregate expression invokes the aggregate
once for each input row.
The second form is the same as the first, since
ALL
is the default.
The third form invokes the aggregate once for each distinct value
of the expression (or distinct set of values, for multiple expressions)
found in the input rows.
The fourth form invokes the aggregate once for each input row; since no
particular input value is specified, it is generally only useful
for the count(*)
aggregate function.
The last form is used with ordered-set aggregate
functions, which are described below.
Most aggregate functions ignore null inputs, so that rows in which one or more of the expression(s) yield null are discarded. This can be assumed to be true, unless otherwise specified, for all built-in aggregates.
For example, count(*)
yields the total number
of input rows; count(f1)
yields the number of
input rows in which f1
is non-null, since
count
ignores nulls; and
count(distinct f1)
yields the number of
distinct non-null values of f1
.
Ordinarily, the input rows are fed to the aggregate function in an
unspecified order. In many cases this does not matter; for example,
min
produces the same result no matter what order it
receives the inputs in. However, some aggregate functions
(such as array_agg
and string_agg
) produce
results that depend on the ordering of the input rows. When using
such an aggregate, the optional order_by_clause
can be
used to specify the desired ordering. The order_by_clause
has the same syntax as for a query-level ORDER BY
clause, as
described in 7.5절, except that its expressions
are always just expressions and cannot be output-column names or numbers.
For example:
SELECT array_agg(a ORDER BY b DESC) FROM table;
When dealing with multiple-argument aggregate functions, note that the
ORDER BY
clause goes after all the aggregate arguments.
For example, write this:
SELECT string_agg(a, ',' ORDER BY a) FROM table;
not this:
SELECT string_agg(a ORDER BY a, ',') FROM table; -- incorrect
The latter is syntactically valid, but it represents a call of a
single-argument aggregate function with two ORDER BY
keys
(the second one being rather useless since it's a constant).
If DISTINCT
is specified in addition to an
order_by_clause
, then all the ORDER BY
expressions must match regular arguments of the aggregate; that is,
you cannot sort on an expression that is not included in the
DISTINCT
list.
The ability to specify both DISTINCT
and ORDER BY
in an aggregate function is a PostgreSQL extension.
Placing ORDER BY
within the aggregate's regular argument
list, as described so far, is used when ordering the input rows for
general-purpose and statistical aggregates, for which ordering is
optional. There is a
subclass of aggregate functions called ordered-set
aggregates for which an order_by_clause
is required, usually because the aggregate's computation is
only sensible in terms of a specific ordering of its input rows.
Typical examples of ordered-set aggregates include rank and percentile
calculations. For an ordered-set aggregate,
the order_by_clause
is written
inside WITHIN GROUP (...)
, as shown in the final syntax
alternative above. The expressions in
the order_by_clause
are evaluated once per
input row just like regular aggregate arguments, sorted as per
the order_by_clause
's requirements, and fed
to the aggregate function as input arguments. (This is unlike the case
for a non-WITHIN GROUP
order_by_clause
,
which is not treated as argument(s) to the aggregate function.) The
argument expressions preceding WITHIN GROUP
, if any, are
called direct arguments to distinguish them from
the aggregated arguments listed in
the order_by_clause
. Unlike regular aggregate
arguments, direct arguments are evaluated only once per aggregate call,
not once per input row. This means that they can contain variables only
if those variables are grouped by GROUP BY
; this restriction
is the same as if the direct arguments were not inside an aggregate
expression at all. Direct arguments are typically used for things like
percentile fractions, which only make sense as a single value per
aggregation calculation. The direct argument list can be empty; in this
case, write just ()
not (*)
.
(PostgreSQL will actually accept either spelling, but
only the first way conforms to the SQL standard.)
An example of an ordered-set aggregate call is:
SELECT percentile_cont(0.5) WITHIN GROUP (ORDER BY income) FROM households; percentile_cont ----------------- 50489
which obtains the 50th percentile, or median, value of
the income
column from table households
.
Here, 0.5
is a direct argument; it would make no sense
for the percentile fraction to be a value varying across rows.
If FILTER
is specified, then only the input
rows for which the filter_clause
evaluates to true are fed to the aggregate function; other rows
are discarded. For example:
SELECT count(*) AS unfiltered, count(*) FILTER (WHERE i < 5) AS filtered FROM generate_series(1,10) AS s(i); unfiltered | filtered ------------+---------- 10 | 4 (1 row)
The predefined aggregate functions are described in 9.21절. Other aggregate functions can be added by the user.
An aggregate expression can only appear in the result list or
HAVING
clause of a SELECT
command.
It is forbidden in other clauses, such as WHERE
,
because those clauses are logically evaluated before the results
of aggregates are formed.
When an aggregate expression appears in a subquery (see
4.2.11절 and
9.23절), the aggregate is normally
evaluated over the rows of the subquery. But an exception occurs
if the aggregate's arguments (and filter_clause
if any) contain only outer-level variables:
the aggregate then belongs to the nearest such outer level, and is
evaluated over the rows of that query. The aggregate expression
as a whole is then an outer reference for the subquery it appears in,
and acts as a constant over any one evaluation of that subquery.
The restriction about
appearing only in the result list or HAVING
clause
applies with respect to the query level that the aggregate belongs to.
A window function call represents the application
of an aggregate-like function over some portion of the rows selected
by a query. Unlike non-window aggregate calls, this is not tied
to grouping of the selected rows into a single output row — each
row remains separate in the query output. However the window function
has access to all the rows that would be part of the current row's
group according to the grouping specification (PARTITION BY
list) of the window function call.
The syntax of a window function call is one of the following:
function_name
([expression
[,expression
... ]]) [ FILTER ( WHEREfilter_clause
) ] OVERwindow_name
function_name
([expression
[,expression
... ]]) [ FILTER ( WHEREfilter_clause
) ] OVER (window_definition
)function_name
( * ) [ FILTER ( WHEREfilter_clause
) ] OVERwindow_name
function_name
( * ) [ FILTER ( WHEREfilter_clause
) ] OVER (window_definition
)
where window_definition
has the syntax
[existing_window_name
] [ PARTITION BYexpression
[, ...] ] [ ORDER BYexpression
[ ASC | DESC | USINGoperator
] [ NULLS { FIRST | LAST } ] [, ...] ] [frame_clause
]
The optional frame_clause
can be one of
{ RANGE | ROWS | GROUPS }frame_start
[frame_exclusion
] { RANGE | ROWS | GROUPS } BETWEENframe_start
ANDframe_end
[frame_exclusion
]
where frame_start
and frame_end
can be one of
UNBOUNDED PRECEDINGoffset
PRECEDING CURRENT ROWoffset
FOLLOWING UNBOUNDED FOLLOWING
and frame_exclusion
can be one of
EXCLUDE CURRENT ROW EXCLUDE GROUP EXCLUDE TIES EXCLUDE NO OTHERS
Here, expression
represents any value
expression that does not itself contain window function calls.
window_name
is a reference to a named window
specification defined in the query's WINDOW
clause.
Alternatively, a full window_definition
can
be given within parentheses, using the same syntax as for defining a
named window in the WINDOW
clause; see the
SELECT reference page for details. It's worth
pointing out that OVER wname
is not exactly equivalent to
OVER (wname ...)
; the latter implies copying and modifying the
window definition, and will be rejected if the referenced window
specification includes a frame clause.
The PARTITION BY
clause groups the rows of the query into
partitions, which are processed separately by the window
function. PARTITION BY
works similarly to a query-level
GROUP BY
clause, except that its expressions are always just
expressions and cannot be output-column names or numbers.
Without PARTITION BY
, all rows produced by the query are
treated as a single partition.
The ORDER BY
clause determines the order in which the rows
of a partition are processed by the window function. It works similarly
to a query-level ORDER BY
clause, but likewise cannot use
output-column names or numbers. Without ORDER BY
, rows are
processed in an unspecified order.
The frame_clause
specifies
the set of rows constituting the window frame, which is a
subset of the current partition, for those window functions that act on
the frame instead of the whole partition. The set of rows in the frame
can vary depending on which row is the current row. The frame can be
specified in RANGE
, ROWS
or GROUPS
mode; in each case, it runs from
the frame_start
to
the frame_end
.
If frame_end
is omitted, the end defaults
to CURRENT ROW
.
A frame_start
of UNBOUNDED PRECEDING
means
that the frame starts with the first row of the partition, and similarly
a frame_end
of UNBOUNDED FOLLOWING
means
that the frame ends with the last row of the partition.
In RANGE
or GROUPS
mode,
a frame_start
of
CURRENT ROW
means the frame starts with the current
row's first peer row (a row that the
window's ORDER BY
clause sorts as equivalent to the
current row), while a frame_end
of
CURRENT ROW
means the frame ends with the current
row's last peer row.
In ROWS
mode, CURRENT ROW
simply
means the current row.
In the offset
PRECEDING
and offset
FOLLOWING
frame
options, the offset
must be an expression not
containing any variables, aggregate functions, or window functions.
The meaning of the offset
depends on the
frame mode:
In ROWS
mode,
the offset
must yield a non-null,
non-negative integer, and the option means that the frame starts or
ends the specified number of rows before or after the current row.
In GROUPS
mode,
the offset
again must yield a non-null,
non-negative integer, and the option means that the frame starts or
ends the specified number of peer groups
before or after the current row's peer group, where a peer group is a
set of rows that are equivalent in the ORDER BY
ordering. (There must be an ORDER BY
clause
in the window definition to use GROUPS
mode.)
In RANGE
mode, these options require that
the ORDER BY
clause specify exactly one column.
The offset
specifies the maximum
difference between the value of that column in the current row and
its value in preceding or following rows of the frame. The data type
of the offset
expression varies depending
on the data type of the ordering column. For numeric ordering
columns it is typically of the same type as the ordering column,
but for datetime ordering columns it is an interval
.
For example, if the ordering column is of type date
or timestamp
, one could write RANGE BETWEEN
'1 day' PRECEDING AND '10 days' FOLLOWING
.
The offset
is still required to be
non-null and non-negative, though the meaning
of “non-negative” depends on its data type.
In any case, the distance to the end of the frame is limited by the distance to the end of the partition, so that for rows near the partition ends the frame might contain fewer rows than elsewhere.
Notice that in both ROWS
and GROUPS
mode, 0 PRECEDING
and 0 FOLLOWING
are equivalent to CURRENT ROW
. This normally holds
in RANGE
mode as well, for an appropriate
data-type-specific meaning of “zero”.
The frame_exclusion
option allows rows around
the current row to be excluded from the frame, even if they would be
included according to the frame start and frame end options.
EXCLUDE CURRENT ROW
excludes the current row from the
frame.
EXCLUDE GROUP
excludes the current row and its
ordering peers from the frame.
EXCLUDE TIES
excludes any peers of the current
row from the frame, but not the current row itself.
EXCLUDE NO OTHERS
simply specifies explicitly the
default behavior of not excluding the current row or its peers.
The default framing option is RANGE UNBOUNDED PRECEDING
,
which is the same as RANGE BETWEEN UNBOUNDED PRECEDING AND
CURRENT ROW
. With ORDER BY
, this sets the frame to be
all rows from the partition start up through the current row's last
ORDER BY
peer. Without ORDER BY
,
this means all rows of the partition are included in the window frame,
since all rows become peers of the current row.
Restrictions are that
frame_start
cannot be UNBOUNDED FOLLOWING
,
frame_end
cannot be UNBOUNDED PRECEDING
,
and the frame_end
choice cannot appear earlier in the
above list of frame_start
and frame_end
options than
the frame_start
choice does — for example
RANGE BETWEEN CURRENT ROW AND
is not allowed.
But, for example, offset
PRECEDINGROWS BETWEEN 7 PRECEDING AND 8
PRECEDING
is allowed, even though it would never select any
rows.
If FILTER
is specified, then only the input
rows for which the filter_clause
evaluates to true are fed to the window function; other rows
are discarded. Only window functions that are aggregates accept
a FILTER
clause.
The built-in window functions are described in 표 9.60. Other window functions can be added by the user. Also, any built-in or user-defined general-purpose or statistical aggregate can be used as a window function. (Ordered-set and hypothetical-set aggregates cannot presently be used as window functions.)
The syntaxes using *
are used for calling parameter-less
aggregate functions as window functions, for example
count(*) OVER (PARTITION BY x ORDER BY y)
.
The asterisk (*
) is customarily not used for
window-specific functions. Window-specific functions do not
allow DISTINCT
or ORDER BY
to be used within the
function argument list.
Window function calls are permitted only in the SELECT
list and the ORDER BY
clause of the query.
More information about window functions can be found in 3.5절, 9.22절, and 7.2.5절.
A type cast specifies a conversion from one data type to another. PostgreSQL accepts two equivalent syntaxes for type casts:
CAST (expression
AStype
)expression
::type
The CAST
syntax conforms to SQL; the syntax with
::
is historical PostgreSQL
usage.
When a cast is applied to a value expression of a known type, it represents a run-time type conversion. The cast will succeed only if a suitable type conversion operation has been defined. Notice that this is subtly different from the use of casts with constants, as shown in 4.1.2.7절. A cast applied to an unadorned string literal represents the initial assignment of a type to a literal constant value, and so it will succeed for any type (if the contents of the string literal are acceptable input syntax for the data type).
An explicit type cast can usually be omitted if there is no ambiguity as to the type that a value expression must produce (for example, when it is assigned to a table column); the system will automatically apply a type cast in such cases. However, automatic casting is only done for casts that are marked “OK to apply implicitly” in the system catalogs. Other casts must be invoked with explicit casting syntax. This restriction is intended to prevent surprising conversions from being applied silently.
It is also possible to specify a type cast using a function-like syntax:
typename
(expression
)
However, this only works for types whose names are also valid as
function names. For example, double precision
cannot be used this way, but the equivalent float8
can. Also, the names interval
, time
, and
timestamp
can only be used in this fashion if they are
double-quoted, because of syntactic conflicts. Therefore, the use of
the function-like cast syntax leads to inconsistencies and should
probably be avoided.
The function-like syntax is in fact just a function call. When one of the two standard cast syntaxes is used to do a run-time conversion, it will internally invoke a registered function to perform the conversion. By convention, these conversion functions have the same name as their output type, and thus the “function-like syntax” is nothing more than a direct invocation of the underlying conversion function. Obviously, this is not something that a portable application should rely on. For further details see CREATE CAST.
The COLLATE
clause overrides the collation of
an expression. It is appended to the expression it applies to:
expr
COLLATEcollation
where collation
is a possibly
schema-qualified identifier. The COLLATE
clause binds tighter than operators; parentheses can be used when
necessary.
If no collation is explicitly specified, the database system either derives a collation from the columns involved in the expression, or it defaults to the default collation of the database if no column is involved in the expression.
The two common uses of the COLLATE
clause are
overriding the sort order in an ORDER BY
clause, for
example:
SELECT a, b, c FROM tbl WHERE ... ORDER BY a COLLATE "C";
and overriding the collation of a function or operator call that has locale-sensitive results, for example:
SELECT * FROM tbl WHERE a > 'foo' COLLATE "C";
Note that in the latter case the COLLATE
clause is
attached to an input argument of the operator we wish to affect.
It doesn't matter which argument of the operator or function call the
COLLATE
clause is attached to, because the collation that is
applied by the operator or function is derived by considering all
arguments, and an explicit COLLATE
clause will override the
collations of all other arguments. (Attaching non-matching
COLLATE
clauses to more than one argument, however, is an
error. For more details see 23.2절.)
Thus, this gives the same result as the previous example:
SELECT * FROM tbl WHERE a COLLATE "C" > 'foo';
But this is an error:
SELECT * FROM tbl WHERE (a > 'foo') COLLATE "C";
because it attempts to apply a collation to the result of the
>
operator, which is of the non-collatable data type
boolean
.
A scalar subquery is an ordinary
SELECT
query in parentheses that returns exactly one
row with one column. (See 7장 for information about writing queries.)
The SELECT
query is executed
and the single returned value is used in the surrounding value expression.
It is an error to use a query that
returns more than one row or more than one column as a scalar subquery.
(But if, during a particular execution, the subquery returns no rows,
there is no error; the scalar result is taken to be null.)
The subquery can refer to variables from the surrounding query,
which will act as constants during any one evaluation of the subquery.
See also 9.23절 for other expressions involving subqueries.
For example, the following finds the largest city population in each state:
SELECT name, (SELECT max(pop) FROM cities WHERE cities.state = states.name) FROM states;
An array constructor is an expression that builds an
array value using values for its member elements. A simple array
constructor
consists of the key word ARRAY
, a left square bracket
[
, a list of expressions (separated by commas) for the
array element values, and finally a right square bracket ]
.
For example:
SELECT ARRAY[1,2,3+4]; array --------- {1,2,7} (1 row)
By default,
the array element type is the common type of the member expressions,
determined using the same rules as for UNION
or
CASE
constructs (see 10.5절).
You can override this by explicitly casting the array constructor to the
desired type, for example:
SELECT ARRAY[1,2,22.7]::integer[]; array ---------- {1,2,23} (1 row)
This has the same effect as casting each expression to the array element type individually. For more on casting, see 4.2.9절.
Multidimensional array values can be built by nesting array
constructors.
In the inner constructors, the key word ARRAY
can
be omitted. For example, these produce the same result:
SELECT ARRAY[ARRAY[1,2], ARRAY[3,4]]; array --------------- {{1,2},{3,4}} (1 row) SELECT ARRAY[[1,2],[3,4]]; array --------------- {{1,2},{3,4}} (1 row)
Since multidimensional arrays must be rectangular, inner constructors
at the same level must produce sub-arrays of identical dimensions.
Any cast applied to the outer ARRAY
constructor propagates
automatically to all the inner constructors.
Multidimensional array constructor elements can be anything yielding
an array of the proper kind, not only a sub-ARRAY
construct.
For example:
CREATE TABLE arr(f1 int[], f2 int[]); INSERT INTO arr VALUES (ARRAY[[1,2],[3,4]], ARRAY[[5,6],[7,8]]); SELECT ARRAY[f1, f2, '{{9,10},{11,12}}'::int[]] FROM arr; array ------------------------------------------------ {{{1,2},{3,4}},{{5,6},{7,8}},{{9,10},{11,12}}} (1 row)
You can construct an empty array, but since it's impossible to have an array with no type, you must explicitly cast your empty array to the desired type. For example:
SELECT ARRAY[]::integer[]; array ------- {} (1 row)
It is also possible to construct an array from the results of a
subquery. In this form, the array constructor is written with the
key word ARRAY
followed by a parenthesized (not
bracketed) subquery. For example:
SELECT ARRAY(SELECT oid FROM pg_proc WHERE proname LIKE 'bytea%'); array ------------------------------------------------------------------ {2011,1954,1948,1952,1951,1244,1950,2005,1949,1953,2006,31,2412} (1 row) SELECT ARRAY(SELECT ARRAY[i, i*2] FROM generate_series(1,5) AS a(i)); array ---------------------------------- {{1,2},{2,4},{3,6},{4,8},{5,10}} (1 row)
The subquery must return a single column. If the subquery's output column is of a non-array type, the resulting one-dimensional array will have an element for each row in the subquery result, with an element type matching that of the subquery's output column. If the subquery's output column is of an array type, the result will be an array of the same type but one higher dimension; in this case all the subquery rows must yield arrays of identical dimensionality, else the result would not be rectangular.
The subscripts of an array value built with ARRAY
always begin with one. For more information about arrays, see
8.15절.
A row constructor is an expression that builds a row value (also
called a composite value) using values
for its member fields. A row constructor consists of the key word
ROW
, a left parenthesis, zero or more
expressions (separated by commas) for the row field values, and finally
a right parenthesis. For example:
SELECT ROW(1,2.5,'this is a test');
The key word ROW
is optional when there is more than one
expression in the list.
A row constructor can include the syntax
rowvalue
.*
,
which will be expanded to a list of the elements of the row value,
just as occurs when the .*
syntax is used at the top level
of a SELECT
list (see 8.16.5절).
For example, if table t
has
columns f1
and f2
, these are the same:
SELECT ROW(t.*, 42) FROM t; SELECT ROW(t.f1, t.f2, 42) FROM t;
Before PostgreSQL 8.2, the
.*
syntax was not expanded in row constructors, so
that writing ROW(t.*, 42)
created a two-field row whose first
field was another row value. The new behavior is usually more useful.
If you need the old behavior of nested row values, write the inner
row value without .*
, for instance
ROW(t, 42)
.
By default, the value created by a ROW
expression is of
an anonymous record type. If necessary, it can be cast to a named
composite type — either the row type of a table, or a composite type
created with CREATE TYPE AS
. An explicit cast might be needed
to avoid ambiguity. For example:
CREATE TABLE mytable(f1 int, f2 float, f3 text); CREATE FUNCTION getf1(mytable) RETURNS int AS 'SELECT $1.f1' LANGUAGE SQL; -- No cast needed since only one getf1() exists SELECT getf1(ROW(1,2.5,'this is a test')); getf1 ------- 1 (1 row) CREATE TYPE myrowtype AS (f1 int, f2 text, f3 numeric); CREATE FUNCTION getf1(myrowtype) RETURNS int AS 'SELECT $1.f1' LANGUAGE SQL; -- Now we need a cast to indicate which function to call: SELECT getf1(ROW(1,2.5,'this is a test')); ERROR: function getf1(record) is not unique SELECT getf1(ROW(1,2.5,'this is a test')::mytable); getf1 ------- 1 (1 row) SELECT getf1(CAST(ROW(11,'this is a test',2.5) AS myrowtype)); getf1 ------- 11 (1 row)
Row constructors can be used to build composite values to be stored
in a composite-type table column, or to be passed to a function that
accepts a composite parameter. Also,
it is possible to compare two row values or test a row with
IS NULL
or IS NOT NULL
, for example:
SELECT ROW(1,2.5,'this is a test') = ROW(1, 3, 'not the same'); SELECT ROW(table.*) IS NULL FROM table; -- detect all-null rows
For more detail see 9.24절. Row constructors can also be used in connection with subqueries, as discussed in 9.23절.
The order of evaluation of subexpressions is not defined. In particular, the inputs of an operator or function are not necessarily evaluated left-to-right or in any other fixed order.
Furthermore, if the result of an expression can be determined by evaluating only some parts of it, then other subexpressions might not be evaluated at all. For instance, if one wrote:
SELECT true OR somefunc();
then somefunc()
would (probably) not be called
at all. The same would be the case if one wrote:
SELECT somefunc() OR true;
Note that this is not the same as the left-to-right “short-circuiting” of Boolean operators that is found in some programming languages.
As a consequence, it is unwise to use functions with side effects
as part of complex expressions. It is particularly dangerous to
rely on side effects or evaluation order in WHERE
and HAVING
clauses,
since those clauses are extensively reprocessed as part of
developing an execution plan. Boolean
expressions (AND
/OR
/NOT
combinations) in those clauses can be reorganized
in any manner allowed by the laws of Boolean algebra.
When it is essential to force evaluation order, a CASE
construct (see 9.18절) can be
used. For example, this is an untrustworthy way of trying to
avoid division by zero in a WHERE
clause:
SELECT ... WHERE x > 0 AND y/x > 1.5;
But this is safe:
SELECT ... WHERE CASE WHEN x > 0 THEN y/x > 1.5 ELSE false END;
A CASE
construct used in this fashion will defeat optimization
attempts, so it should only be done when necessary. (In this particular
example, it would be better to sidestep the problem by writing
y > 1.5*x
instead.)
CASE
is not a cure-all for such issues, however.
One limitation of the technique illustrated above is that it does not
prevent early evaluation of constant subexpressions.
As described in 37.7절, functions and
operators marked IMMUTABLE
can be evaluated when
the query is planned rather than when it is executed. Thus for example
SELECT CASE WHEN x > 0 THEN x ELSE 1/0 END FROM tab;
is likely to result in a division-by-zero failure due to the planner
trying to simplify the constant subexpression,
even if every row in the table has x > 0
so that the
ELSE
arm would never be entered at run time.
While that particular example might seem silly, related cases that don't
obviously involve constants can occur in queries executed within
functions, since the values of function arguments and local variables
can be inserted into queries as constants for planning purposes.
Within PL/pgSQL functions, for example, using an
IF
-THEN
-ELSE
statement to protect
a risky computation is much safer than just nesting it in a
CASE
expression.
Another limitation of the same kind is that a CASE
cannot
prevent evaluation of an aggregate expression contained within it,
because aggregate expressions are computed before other
expressions in a SELECT
list or HAVING
clause
are considered. For example, the following query can cause a
division-by-zero error despite seemingly having protected against it:
SELECT CASE WHEN min(employees) > 0 THEN avg(expenses / employees) END FROM departments;
The min()
and avg()
aggregates are computed
concurrently over all the input rows, so if any row
has employees
equal to zero, the division-by-zero error
will occur before there is any opportunity to test the result of
min()
. Instead, use a WHERE
or FILTER
clause to prevent problematic input rows from
reaching an aggregate function in the first place.